
"""simple docstring"""




import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  return EnvironmentCommand()




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  return EnvironmentCommand(args.accelerate_config_file    )







class       __magic_name__							(							snake_case_			):
       @staticmethod
       def 				lowercase_				(  A_							)  ->						Any:





         """simple docstring"""




         _lowercase:      Optional[int]   			=  parser.add_parser('''env'''							)
         download_parser.set_defaults(func=lowerCAmelCase__							)
         download_parser.add_argument(
             '''--accelerate-config_file'''			,					default=lowerCAmelCase__			,					help='''The accelerate config file to use for the default values in the launching script.'''			,					)
         download_parser.set_defaults(func=lowerCAmelCase__							)
       def __init__(  self			,					A_			,					*A_							)  ->						Dict:





         """simple docstring"""




         _lowercase:      Union[str, Any]   			=  accelerate_config_file
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         _lowercase:      str   			=  '''not installed'''
         if is_safetensors_available():
           import safetensors

           _lowercase:      Optional[Any]   			=  safetensors.__version__
         elif importlib.util.find_spec('''safetensors'''							) is not None:
           import safetensors

           _lowercase:      Tuple   			=  f'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

         _lowercase:      Optional[int]   			=  '''not installed'''
         _lowercase:      Union[str, Any]   			=  '''not found'''
         if is_accelerate_available():
           import accelerate
           from accelerate.commands.config import default_config_file, load_config_from_file

           _lowercase:      List[str]   			=  accelerate.__version__
           # Get the default from the config file.
           if self._accelerate_config_file is not None or os.path.isfile(lowerCAmelCase__							):
             _lowercase:      Union[str, Any]   			=  load_config_from_file(self._accelerate_config_file							).to_dict()

           _lowercase:      List[str]   			=  (
               '''\n'''.join([f'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]							)
               if isinstance(lowerCAmelCase__			,					lowerCAmelCase__							)
               else f'''\t{accelerate_config}'''
           )

         _lowercase:      Tuple   			=  '''not installed'''
         _lowercase:      Optional[int]   			=  '''NA'''
         if is_torch_available():
           import torch

           _lowercase:      int   			=  torch.__version__
           _lowercase:      List[str]   			=  torch.cuda.is_available()

         _lowercase:      Dict   			=  '''not installed'''
         _lowercase:      int   			=  '''NA'''
         if is_tf_available():
           import tensorflow as tf

           _lowercase:      Any   			=  tf.__version__
           try:
             # deprecated in v2.1
             _lowercase:      List[str]   			=  tf.test.is_gpu_available()
           except AttributeError:
             # returns list of devices, convert to bool
             _lowercase:      Dict   			=  bool(tf.config.list_physical_devices('''GPU'''							)							)

         _lowercase:      List[Any]   			=  '''not installed'''
         _lowercase:      List[str]   			=  '''not installed'''
         _lowercase:      str   			=  '''not installed'''
         _lowercase:      List[Any]   			=  '''NA'''
         if is_flax_available():
           import flax
           import jax
           import jaxlib

           _lowercase:      Union[str, Any]   			=  flax.__version__
           _lowercase:      Dict   			=  jax.__version__
           _lowercase:      List[Any]   			=  jaxlib.__version__
           _lowercase:      Any   			=  jax.lib.xla_bridge.get_backend().platform

         _lowercase:      int   			=  {
             '''`transformers` version''': version,
             '''Platform''': platform.platform(),
             '''Python version''': platform.python_version(),
             '''Huggingface_hub version''': huggingface_hub.__version__,
             '''Safetensors version''': f'''{safetensors_version}''',
             '''Accelerate version''': f'''{accelerate_version}''',
             '''Accelerate config''': f'''{accelerate_config_str}''',
             '''PyTorch version (GPU?)''': f'''{pt_version} ({pt_cuda_available})''',
             '''Tensorflow version (GPU?)''': f'''{tf_version} ({tf_cuda_available})''',
             '''Flax version (CPU?/GPU?/TPU?)''': f'''{flax_version} ({jax_backend})''',
             '''Jax version''': f'''{jax_version}''',
             '''JaxLib version''': f'''{jaxlib_version}''',
             '''Using GPU in script?''': '''<fill in>''',
             '''Using distributed or parallel set-up in script?''': '''<fill in>''',
         }

         print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'''							)
         print(self.format_dict(lowerCAmelCase__							)							)

         return info



       @staticmethod
       def 				lowercase_				(  A_							)  ->						List[Any]:





         """simple docstring"""




         return "\n".join([f'''- {prop}: {val}''' for prop, val in d.items()]							) + "\n"





'''simple docstring'''



import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
							import tensorflow as tf
							from tensorflow.python.eager import context
							from tensorflow.python.framework import ops

							from transformers import GradientAccumulator, create_optimizer
@require_tf
class 						lowerCamelCase_				(       unittest.TestCase      ):







						def        __lowercase     (	self :   List[Any]					,     lowerCAmelCase__ :   str					,     lowerCAmelCase__ :   Dict					,     lowerCAmelCase__ :   int					):

													"""simple docstring"""
													self.assertEqual(len(lowerCAmelCase__					)					,     len(lowerCAmelCase__					)					)
													for a, b in zip(lowerCAmelCase__					,     lowerCAmelCase__					):
																				self.assertAlmostEqual(lowerCAmelCase__					,     lowerCAmelCase__					,     delta=lowerCAmelCase__					)







						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[Any]         =      GradientAccumulator()
													accumulator([tf.constant([1.0, 2.0]					)]					)
													accumulator([tf.constant([-2.0, 1.0]					)]					)
													accumulator([tf.constant([-1.0, 2.0]					)]					)
													with self.assertRaises(lowerCAmelCase__					):
																				accumulator([tf.constant([1.0, 1.0]					), tf.constant([2.0, 2.0]					)]					)
													self.assertEqual(accumulator.step					,     3					)
													self.assertEqual(len(accumulator.gradients					)					,     1					)
													self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()					,     [-2.0, 5.0]					,     tol=1e-2					)
													accumulator.reset()
													self.assertEqual(accumulator.step					,     0					)
													self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()					,     [0.0, 0.0]					,     tol=1e-2					)







						def        __lowercase     (	self :   List[str]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      None
													ops.enable_eager_execution_internal()
													SCREAMING_SNAKE_CASE       :   List[Any]         =      tf.config.list_physical_devices('''CPU'''					)
													if len(lowerCAmelCase__					) == 1:
																				tf.config.set_logical_device_configuration(
																				    physical_devices[0]					,     [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      tf.config.list_logical_devices(device_type='''CPU'''					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      tf.distribute.MirroredStrategy(devices=devices[:2]					)

													with strategy.scope():
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      GradientAccumulator()
																				SCREAMING_SNAKE_CASE       :   Dict         =      tf.Variable([4.0, 3.0]					)
																				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Optional[int]         =      create_optimizer(5e-5					,     10					,     5					)
																				SCREAMING_SNAKE_CASE       :   List[Any]         =      tf.Variable([0.0, 0.0]					,     trainable=lowerCAmelCase__					)

													def accumulate_on_replica(lowerCAmelCase__ :   List[str]					):
																				accumulator([gradient]					)

													def apply_on_replica():
																				optimizer.apply_gradients(list(zip(accumulator.gradients					,     [variable]					)					)					)

													@tf.function
													def accumulate(lowerCAmelCase__ :   str					,     lowerCAmelCase__ :   List[Any]					):
																				with strategy.scope():
																											SCREAMING_SNAKE_CASE       :   Optional[Any]         =      strategy.experimental_local_results(lowerCAmelCase__					)
																											local_variables[0].assign(lowerCAmelCase__					)
																											local_variables[1].assign(lowerCAmelCase__					)
																											strategy.run(lowerCAmelCase__					,     args=(gradient_placeholder,)					)

													@tf.function
													def apply_grad():
																				with strategy.scope():
																											strategy.run(lowerCAmelCase__					)

													def _check_local_values(lowerCAmelCase__ :   Dict					,     lowerCAmelCase__ :   int					):
																				SCREAMING_SNAKE_CASE       :   Tuple         =      strategy.experimental_local_results(accumulator._gradients[0]					)
																				self.assertListAlmostEqual(values[0].value()					,     lowerCAmelCase__					,     tol=1e-2					)
																				self.assertListAlmostEqual(values[1].value()					,     lowerCAmelCase__					,     tol=1e-2					)

													accumulate([1.0, 2.0]					,     [-1.0, 1.0]					)
													accumulate([3.0, -1.0]					,     [-1.0, -1.0]					)
													accumulate([-2.0, 2.0]					,     [3.0, -2.0]					)
													self.assertEqual(accumulator.step					,     3					)
													_check_local_values([2.0, 3.0]					,     [1.0, -2.0]					)
													apply_grad()
													self.assertListAlmostEqual(variable.value()					,     [4.0, 3.0]					,     tol=1e-2					)
													accumulator.reset()
													self.assertEqual(accumulator.step					,     0					)
													_check_local_values([0.0, 0.0]					,     [0.0, 0.0]					)




